
In Eq. (REF ), the image features \(I \in \mathbb {R}^{N_\text{I} \times d}\)  are first used to find the most relevant topics and filter out the irrelevant topics, resulting in \(\hat{T} \in \mathbb {R}^{N_\text{I} \times d}\) . Then the attended topics \(\hat{T}\)  are further used to mine topic related image features \(\hat{I} \in \mathbb {R}^{N_\text{I} \times d}\) . Since \(T\)  contains the abnormality topics, the topic related image features can be referred as the abnormal regions. In this way, we can not only obtain the abnormal regions, but also align the attended abnormal regions with the relevant topics [1]}, which imitates the working patterns of radiologists to assign the disease topic tags to the abnormal regions when examining the abnormal regions. It is worth noting that if we change the order from \(I\rightarrow \hat{T}\rightarrow \hat{I}\)  to \(T\rightarrow \hat{I}\rightarrow \hat{T}\) , the performance will drop. The reason is presumably due to the noisy topics, which contains a large amount of irrelevant topics in \(T\) , thus we should first filter out the irrelevant topics as the presented in Eq. (REF ).
